Data Visualisation Final Project
Group 7
2023-11-20
Load libraries and fonts
library(tidyverse)
library(readxl)
library(vroom)
library(ggtext)
library(ggrepel)
library(patchwork)
library(gghighlight)
library(skimr)
library(showtext)
library(wbstats)
library(ggforce)
library(directlabels)
library(plotly)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
font_add_google("Lato")
font_add_google("Roboto")
showtext_auto()Introduction
In this project, we will analyze how Covid-19 disrupted the global shipping industry and what implications the Covid-19 impact had on the industry and the world as a whole. To do this, we have collected multiple datasets on container trading, dry bulk trading (coal, grain) as well as on tanker trading (e.g., oil, gas), and we have used them to explore different hypothesis and then explore the opportunity to create a story around the impact that Covid had on the global shipping industry, and its implications. The datasets mainly come from UNCTAD’s data center, but also from sources such as Bloomberg, Statista, and Clarkson Research. We started our project by going through UNCTAD’s yearly reviews of maritime transport, which served as a great foundation to understand what kind of data we can retrieve, what visualisations that could make sense and what story could be created. Furthermore, we have created our own datasets with major ports based on coordinates from Google Maps and one dataset with illustrative trade routes to create the map, also based on coordinates retrieved from Google Maps.
This document will begin with loading the data, and then start creating visualizations for our project.
Load & Clean data
container_throughput <- read_csv(here::here("data","Container_port_throughput.csv"))
container_tradeflows <- read_excel(here::here("data", "Container_trade_flows.xlsx"))
scfi_index <- read_excel(here::here("data", "Shanghai Containerized Freight Index.xlsx"))
containers_EBIT <- read_excel(here::here("data", "Statista_avg_Ebit_margin_containers.xlsx"))
container_routes <- read_excel(here::here("data", "TradeRoutes.xlsx"))
container_wait_times <- read_csv(here::here("data", "Container_wait_times.csv"))
major_ports <- read_csv(here::here("data", "major_container_ports.csv"))
#Clean the datasets: use janitor::clean_names() and manually make some changes
container_throughput <- container_throughput %>%
janitor:: clean_names() %>%
rename(TEU = teu_twenty_foot_equivalent_unit)
scfi_index <- scfi_index %>%
janitor:: clean_names()
containers_EBIT <- containers_EBIT %>%
janitor:: clean_names()
container_wait_times <- container_wait_times %>%
janitor::clean_names()
#Here we pivot_longer to make it easier to plot and do calculations
container_tradeflows <- container_tradeflows %>%
pivot_longer(cols = 8:20,
names_to = "year",
values_to = "TEU") %>%
janitor:: clean_names()Plot 1: Map of Major Trade Routes for Container Shipping
In this plot, we will create an illustrative plot of the top 5 trade routes where most of the worlds inter-continental trading occur. To simplify the visualisation, we have only used one port to represent a whole continent, while in reality this is not entirely true, the simplification will convey the same message while keeping the details of the map interpretable. The choice of ports to represent the continent has been done by choosing the largest port in terms of container ships throughput. The choices are presented below:
- Asia: Shanghai
- Europe: Rotterdam
- Australasia & Oceania: Melbourne
- Sub-Saharan Africa: Dunbar
- Indian Sub Continent & Middle East: Mundra
- North America: Los Angeles and New York
- South America: Santos
We will need some initial data wrangling to be able to make the map, to join the trade magnitude dataset with coordinates and illustrative trade routes.
#Set colors
my_colours <- c("#fd7f6f", "#7eb0d5", "#011f5f","#8cc24e", "#ffb55a")
#Join the tradeflows dataset with our dataset of major ports
container_tradeflows_ports <- container_tradeflows %>%
#first we'll get the port of the origin
left_join(major_ports, by = c("origin" = "region")) %>%
rename(origin_port = port,
origin_lat = lat,
origin_lng = lng) %>%
#Then we'll get the port for the destination
left_join(major_ports, by = c("destination" = "region")) %>%
rename(dest_port = port,
dest_lat = lat,
dest_lng = lng) %>%
#We want to remove intra-continental trade routes for this plot
mutate(dest_lat = ifelse(origin_lat == dest_lat, NA, dest_lat),
dest_lng = ifelse(origin_lng == dest_lng, NA, dest_lng)) %>%
#Filter for NAs and only choose the year of 2022
filter(!is.na(origin),
year == 2022)
#Now, we will create a name for each trade route, that goes both ways. That is, Asia to Europe will have the same name as Europe to Asia
container_tradeflows_routes <- container_tradeflows_ports %>%
filter(origin != destination) %>%
#This is a messy code but essentially it gives this two-way name for each trade route
mutate(origin_dest = ifelse(origin == "Asia", paste(origin, destination, sep = "-"),
ifelse(origin == "Australasia & Oceania" & destination != "Asia", paste(origin, destination, sep = "-"),
ifelse(origin == "Europe" & !destination %in% c("Asia", "Australasia & Oceania"), paste(origin, destination, sep = "-"),
ifelse(origin == "Indian Sub Cont. & Middle East" & !destination %in% c("Asia", "Australasia & Oceania", "Europe"), paste(origin, destination, sep = "-"),
ifelse(origin == "North America" & !destination %in% c("Asia", "Australasia & Oceania", "Europe", "Indian Sub Cont. & Middle East"), paste(origin, destination, sep = "-"),
ifelse(origin == "South & Central America" & !destination %in% c("Asia", "Australasia & Oceania", "Europe", "Indian Sub Cont. & Middle East", "North America"), paste(origin, destination, sep = "-"), paste(destination, origin, sep = "-"))))))))
#Now, we'll add the coordinates for the trade routes to this dataset
container_tradeflows_routes <- container_tradeflows_routes %>%
#Group by two-way name and calculate the total TEU traded on that route
group_by(origin_dest) %>%
mutate(total_teu = sum(teu)) %>%
ungroup() %>%
#Join it with our trade route coordinates dataset
left_join(container_routes, by = "origin_dest") %>%
#Remove unnecessary variables and rename variables
select(-origin_lat.x, -origin_lng.x, -dest_lat.x, -dest_lng.x) %>%
rename(origin_lat = origin_lat.y, origin_lng = origin_lng.y,
dest_lat = dest_lat.y, dest_lng = dest_lng.y) %>%
select(-year, -origin_port, -dest_port, -Origin, -Destination) %>%
#Since we now will have duplicate entries, we will only keep the distinct ones
distinct(origin_dest, .keep_all = TRUE)
#In our graph, we will only show the top 5 routes, so we will extract these from our tibble
container_routes_for_graph <- container_tradeflows_routes %>%
slice_max(order_by = total_teu, n = 5) %>%
#Now, we will have some overlap of lines, which we don't want, hence we will move Asia-Europe line down 5 degrees
mutate(origin_lat = ifelse(origin_dest == "Asia-Europe", origin_lat - 5, origin_lat),
stop1_lat = ifelse(origin_dest == "Asia-Europe", stop1_lat - 5, stop1_lat),
stop2_lat = ifelse(origin_dest == "Asia-Europe", stop2_lat - 5, stop2_lat),
stop3_lat = ifelse(origin_dest == "Asia-Europe", stop3_lat - 5, stop3_lat))
#Calculate how much of the global inter-continental trade, the top 5 routes account for
#This calculates the TEU of top 5 routes
top5_routes_TEU <- container_routes_for_graph %>%
summarise(TEU = sum(total_teu)) %>%
pull()
#This calculate the total inter-continental TEU
allroutes_TEU <- container_tradeflows_routes %>%
summarise(TEU = sum(total_teu)) %>%
pull()
top5_perc_of_global <- 100*round(top5_routes_TEU / allroutes_TEU, 2)
#Finally, let's create the map
#Start by creating the world
world <- ne_countries(scale = "medium", returnclass = "sf") %>%
filter(name != "Antarctica")
#And now we create the map
container_map <- ggplot(data = world) +
geom_sf(
mapping = aes(
geometry = geometry
),
colour = "white",
show.legend = FALSE
) +
#Remove coordinates
coord_sf(datum = NA) +
#Add points for all ports that will be included in the port
geom_point(data = container_tradeflows_ports,
aes(x = origin_lng, y = origin_lat),
size = 2,
color = "tomato") +
theme_void() +
#Set the colors of the plot to our pre-defined color palette
scale_color_manual(values = my_colours) +
#Now, we will add curves in the map for each trade route. Since trade routes have to go through water, we have to make them in steps to avoid having them go over land
#First curve is from the origin to our defined "stop1", which can be thought of as the "first" turn
geom_curve(
data = container_routes_for_graph,
aes(x = origin_lng, y = origin_lat, xend = stop1_lng, yend = stop1_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both"),
position = position_dodge2(.5)
) +
#Second curve is from the first turn to the second
geom_curve(
data = container_routes_for_graph,
aes(x = stop1_lng, y = stop1_lat, xend = stop2_lng, yend = stop2_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Third curve is from the second turn to the third
geom_curve(
data = container_routes_for_graph,
aes(x = stop2_lng, y = stop2_lat, xend = stop3_lng, yend = stop3_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Fourth curve is from the third turn to the fourth
geom_curve(
data = container_routes_for_graph,
aes(x = stop3_lng, y = stop3_lat, xend = stop4_lng, yend = stop4_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Fifth curve is from the fourth turn to the fifth
geom_curve(
data = container_routes_for_graph,
aes(x = stop4_lng, y = stop4_lat, xend = stop5_lng, yend = stop5_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Sixth curve is from the fifth turn to the sixth
geom_curve(
data = container_routes_for_graph,
aes(x = stop5_lng, y = stop5_lat, xend = stop6_lng, yend = stop6_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Seventh curve is from the sixth turn to the Seventh
geom_curve(
data = container_routes_for_graph,
aes(x = stop6_lng, y = stop6_lat, xend = stop7_lng, yend = stop7_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Eigth curve is from the seventh turn to the last
geom_curve(
data = container_routes_for_graph,
aes(x = stop7_lng, y = stop7_lat, xend = stop8_lng, yend = stop8_lat, color = factor(origin_dest)),
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed", ends = "both")
) +
#Since Asia and North America would trade over the pacific, we have to add manual trade routes to avoid having a line all across the map
#This line will start in the very west part of the map and then go to the port of LA
geom_curve(
data = data.frame(x = -180.25 , y = 33.24, xend = -118.26, yend = 33.74),
aes(x = x, y = y, xend = xend, yend = yend),
color = "#011f5f",
curvature = 0,
size = 2,
arrow = arrow(length = unit(3, "pt"), type = "closed"),
ends = "both") +
#Remove legend
theme(legend.position = "null") +
#Set font to Roboto
theme(text = element_text(family = "Roboto")) +
#Set title, subtitle and caption size, font and colors
theme(plot.title = element_text(size = 52, family = "Roboto"),
plot.subtitle = element_text(size = 44, color = "grey50", family = "Roboto"),
plot.caption = element_text(size = 32, family = "Roboto")) +
#Add title and subtitle to plot
labs(title = paste0("Top 5 major trade routes account for ", top5_perc_of_global, "% of global container trade"),
subtitle = "Top 5 global container routes by total TEU, 2022",
caption = "Source: Bloomberg") +
#Now, we will add labels to the trade routes, that display the amount traded on percent of global inter-continental trade
#For North America - Europe route
geom_label(
data = data.frame(x = -40.02, y = 47.58, label = "16,230 kTEU\n 10% of global trade"),
aes(x = x, y =y, label = label),
colour = "#8cc24e",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 14,
family = "Roboto",
label.padding = unit(1, "lines")
) +
#For Asia-Europe route
geom_label(
data = data.frame(x = 35.64, y = 35.12, label = "22,130 kTEU\n 14% of global trade"),
aes(x = x, y =y, label = label),
colour = "#fd7f6f",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 14,
family = "Roboto",
label.padding = unit(1, "lines")
) +
#For North America-South America route
geom_label(
data = data.frame(x = -36.99, y = 12.26, label = "11,410 kTEU\n 7% of global trade"),
aes(x = x, y =y, label = label),
colour = "#ffb55a",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 14,
family = "Roboto",
label.padding = unit(1, "lines")
) +
#For Asia-Indian Sub Continent & Middle East route
geom_label(
data = data.frame(x = 92, y = 15.54, label = "10,900 kTEU\n 7% of global trade"),
aes(x = x, y =y, label = label),
colour = "#7eb0d5",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 14,
family = "Roboto",
label.padding = unit(1, "lines")
) +
#For Asia - North America route
geom_label(
data = data.frame(x = 147.70, y = 28.88, label = "56,000 kTEU\n 34% of global trade"),
aes(x = x, y =y, label = label),
colour = "#011f5f",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 14,
family = "Roboto",
label.padding = unit(1, "lines")
)
#Display the map
container_map
Plot 2: Development of Container Throughput over Time
In this plot, we will create a visualization of how the yearly global container port throughput has developed over time. We will do this to see if and how Covid-19 affected the container trade in ports. Our hypothesis was that the port throughput decreased materially because of Covid-19 safety measures, but this turned out to be wrong, since port throughput only experienced a minor stagnation.
## $scipen
## [1] 0#Set our colour palette
my_colours <- c("#fd7f6f", "#7eb0d5","#8cc24e", "#ffb55a", "#bd7ebe", "#011f5f")
#Set a label we will use in our plot
label = "Covid-19 pandemic \n 2020-2022"
#To avoid having a very messy plot, we have chosen some important regions for container trade as well as the world. It is for these regions we will plot the port throughput over time.
locations <- c("Europe", "Asia", "Northern America", "China", "World", "North America", "South America")
#Create the graph
container_throughput_plot <- container_throughput %>%
#Only include the regions we want to
filter(economy_label %in% locations) %>%
#Create an additional variable to be able to create labels at the end of the plot
mutate(name_lab = if_else(year == 2021, economy_label, NA_character_)) %>%
#Start the plot
ggplot(aes(x = year, y = TEU/1000, group = economy_label, color = economy_label)) +
#Some code for creating manual gridlines, we do this to make the gridlines shorter for aesthetic purposes
geom_segment(
data = data.frame(x = -Inf, y = 1000000, xend = 2021, yend = 1000000),
aes(x = x, y = y, xend = xend, yend = yend),
color = "grey90",
inherit.aes = FALSE
) +
geom_segment(
data = data.frame(x = -Inf, y = 750000, xend = 2021, yend = 750000),
aes(x = x, y = y, xend = xend, yend = yend),
color = "grey90",
inherit.aes = FALSE
) +
geom_segment(
data = data.frame(x = -Inf, y = 500000, xend = 2021, yend = 500000),
aes(x = x, y = y, xend = xend, yend = yend),
color = "grey90",
inherit.aes = FALSE
) +
geom_segment(
data = data.frame(x = -Inf, y = 250000, xend = 2021, yend = 250000),
aes(x = x, y = y, xend = xend, yend = yend),
color = "grey90",
inherit.aes = FALSE
) +
geom_segment(
data = data.frame(x = -Inf, y = 0, xend = 2021, yend = 0),
aes(x = x, y = y, xend = xend, yend = yend),
color = "grey90",
inherit.aes = FALSE
) +
#Create a grey rectangel that covers the Covid-19 pandemic period
geom_rect(
xmin = 2019.5,
xmax = 2021.5,
ymin = -Inf,
ymax = Inf,
fill = "grey90",
alpha = 0.1,
inherit.aes = FALSE
) +
#Now we add the line plot, by setting them here and not before we ensure that they are above the annotations we added
geom_line(size = 3) +
#Add our pre-defined colors
scale_colour_manual(values = my_colours) +
#Remove borders and background
theme_minimal() +
#Add title, subtitle, axis labels and source
labs(title = "Covid-19 had No Material Impact on Yearly Port Throughput...",
subtitle = "Total port throughput in twenty-foot equivalents (TEU), 2010-2022",
x = "Year",
y = "'000 TEU",
caption = "Source: UNCTAD") +
#Set title, subtitle and caption size, font and colors
theme(plot.title = element_text(size = 52, family = "Roboto"),
plot.subtitle = element_text(size = 44, color = "grey50", family = "Roboto"),
plot.caption = element_text(size = 32, family = "Roboto")) +
#Left-align the title
theme(plot.title.position = "plot") +
#Remove gridlines
theme(panel.grid = element_blank()) +
#Set the limits and breaks as well as formatting for the y and x axis
scale_y_continuous(label = scales::comma, limits = c(0, 1050000)) +
scale_x_continuous(limits = c(2010, 2024), breaks = seq(2010, 2022, by = 2)) +
#Increase the size of the axis labels
theme(axis.text=element_text(size=44),
axis.title=element_text(size=44,face="bold")) +
#Set font to Roboto
theme(text = element_text(family = "Roboto")) +
#Add annotation for the Covid-19 period
geom_label(
data = data.frame(x = 2020.5, y = 1000000, label = label),
aes(x = x, y = y, label = label),
colour = "grey15",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 16,
label.padding = unit(1, "lines")
) +
#Add the data labels at the end of the line and remove the legend
geom_text_repel(
aes(color = economy_label, label = name_lab),
fontface = "bold",
size = 16,
direction = "y",
xlim = c(2022, NA),
hjust = 0,
segment.size = .7,
segment.alpha = .5,
segment.linetype = "dotted",
box.padding = 0.1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20
) +
theme(legend.position = "null")
#Display the plot
container_throughput_plot
Plot 3: Average Waiting Time in Ports
Even though the port throughput wasn’t materially impacted by Covid-19 lockdowns and safety measures, we’ve heard that the congestion and the waiting times for vessels increased during the pandemic. Hence, the purpose of this plot is to visualize this impact, and we have chosen to show how the pandemic impacted the average waiting time in port for vessels. The plot below shows how the average waiting time in ports have developed over time split up between developing nations and developed nations.
#Create a vector of month abbreviations to map it to their corresponding month number
month_abbrev <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
#Set label to be added to the plot
label = "Covid-19 pandemic\n 2020-2022"
#Create the plot of average wait times in ports
wait_time_plot <- container_wait_times %>%
#Pivot longer to simplify plotting the graph
pivot_longer(2:3,
names_to = "country_type",
values_to = "avg_wait_time") %>%
#Make some changes to the date part of the tibble
mutate(month_name = substr(date, 1, 3),
year = substr(date, 5, 9),
month_num = match(month_name, month_abbrev),
date = ymd(paste(year, month_num + 1, 01, sep = "-")),
date = date - 1) %>%
#Create the plot
ggplot(aes(x = date, y = avg_wait_time, group = country_type, color = country_type)) +
#Create grey box that spans over the time period of the pandemic
geom_rect(
xmin = ymd("2020-03-01"),
xmax = ymd("2022-06-01"),
ymin = -Inf,
ymax = Inf,
fill = "grey90",
alpha = 0.2,
inherit.aes = FALSE
) +
#Initialize line plot
geom_line(size = 2) +
#Remove background and borders
theme_minimal() +
#Set tile, subtitle, axis labels, source and legend title
labs(title = "...but Significantly Increased the Average Wait Time in Ports",
subtitle = "Average wait time in ports for container vessels, 2016-2023",
x = "Year",
y = "Hours",
caption = "Source: Clarkson Research",
color = "") +
#Increase size of axis labels
theme(axis.text = element_text(size=40),
axis.title = element_text(size=40,face="bold")) +
#Set font to Roboto
theme(text = element_text(family = "Roboto")) +
#Set title, subtitle and caption size, font and colors
theme(plot.title = element_text(size = 52, family = "Roboto"),
plot.subtitle = element_text(size = 46, color = "grey50", family = "Roboto"),
plot.caption = element_text(size = 32, family = "Roboto")) +
#Left-align title
theme(plot.title.position = "plot") +
#Remove vertical gridlines and minor horizontal gridlines
theme(panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()) +
#Set the colors of the plot and legend entries
scale_color_manual(
values = c(developing_countries = "#011f5f", developed_countries = "#fd7f6f"),
labels = c(developing_countries = "Developing Countries", developed_countries = "Developed Countries")
) +
#Change position of legend to be bottom of the chart
theme(legend.position = "bottom") +
#Set limits of y-axis
scale_y_continuous(limits = c(0, 15)) +
#Add label of the Covid-19 pandemic in the plot
geom_label(
data = data.frame(x = ymd("2021-04-15"), y = 14.5, label = label),
aes(x = x, y = y, label = label),
colour = "grey15",
hjust = 0.5,
lineheight = .8,
inherit.aes = FALSE,
size = 16,
label.padding = unit(1, "lines")
) +
#Increase size of legend
theme(legend.key.size = unit(1, "cm"),
legend.text = element_text(size = 40))
#Display the plot
wait_time_plot
Plot 4: Container Freight Prices: Shanghai Containerized Freight Index
During the pandemic, we know that supply chains were significantly affected by increasing congestion and waiting times as well as a lack of containers following Covid-19 lockdowns. Both of these factors drove the price up of containerized freight, making it very expensive to ship products across the world. The below graph examines the Shanghai Containerized Freight Index’s development over time to see if it captures this effect. The index shows the most current freight prices for container transport from main ports in China, and can thus be seen as a spot price for containerized freight
#Set annotations we will add to the plot
annotation = "March 11, 2020: WHO declares \n that the Covid-19 outbreak \n is a global pandemic"
annotation2 = "Reversal in the balance of \n supply and demand due to \n macroeconomic uncertainty \n and end of Covid-19"
scfi <- scfi_index %>%
#Create the line plot of the development of the index
ggplot(aes(x = date, y = mid_price)) +
geom_line(color = "#011f5f", size = 2) +
#Remove background and border of the plot
theme_minimal() +
#Remove vertical gridlines and minor horizontal gridlines
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()) +
#Add title, subtitle, axis labels and source
labs(title = "The supply chain issues following Covid-19 significantly increased the price of container freight",
subtitle = "Shanghai Containerized Freight Index, 2014-2023",
x = "Date",
y = "Price",
caption = "Source: Bloomberg") +
#Left-align title
theme(plot.title.position = "plot") +
#Set title, subtitle and caption size, font and colors
theme(plot.title = element_text(size = 52, family = "Roboto"),
plot.subtitle = element_text(size = 46, color = "grey50", family = "Roboto"),
plot.caption = element_text(size = 32, family = "Roboto")) +
#Add curve for annotation 1, that highlights the start of the price surge during Covid-19
geom_curve(
data = data.frame(x = as.POSIXct("2019-03-11"), y = 3000, xend = as.POSIXct("2020-03-11"), yend = 920),
mapping = aes(x = x, y = y, xend = xend, yend = yend),
colour = "grey15" ,
size = 1,
curvature = -0.25,
inherit.aes = FALSE,
#angle = 170,
arrow = arrow(length = unit(2, "mm"), type = "closed")
) +
#Add the text next to the start of this curve
geom_text(
data = data.frame(x = as.POSIXct("2019-02-11"), y = 3300, label = annotation),
aes(x = x, y = y, label = label),
colour="grey15",
hjust = 0.5,
lineheight = 0.8,
inherit.aes = FALSE,
size = 14
) +
#Add the curve for annotation 2, that highlights the reversal in supply and demand that decreased the price of freight
geom_curve(
data = data.frame(x = as.POSIXct("2023-03-01"), y = 4500, xend = as.POSIXct("2022-07-01"), yend = 4250),
mapping = aes(x = x, y = y, xend = xend, yend = yend),
colour = "grey15" ,
size = 1,
curvature = -0.25,
inherit.aes = FALSE,
#angle = 170,
arrow = arrow(length = unit(2, "mm"), type = "closed")
) +
#Add the text next to this curve
geom_text(
data = data.frame(x = as.POSIXct("2023-05-01"), y = 4850, label = annotation2),
aes(x = x, y = y, label = label),
colour="grey15",
hjust = 0.5,
lineheight = 0.8,
inherit.aes = FALSE,
size = 14
) +
#Set font to Roboto
theme(text = element_text(family = "Roboto")) +
#Increase size of axis
theme(axis.text=element_text(size=40),
axis.title=element_text(size=40,face="bold"))
#Display the plot
scfi
Plot 5: EBIT margins of top container carriers
We are now curious to see how the container carriers were affected by this price surge. Our hypothesis was that one of the factors driving up the price of freight was increasing costs of container shipment companies. If this was correct, their profit margins should’ve stayed relatively constant throughout the pandemic. If the profit margins increased, on the other hand, that would mean that they benefitted from the price surge and were able to pass on all increasing costs as well as gain even more money from the problems in the shipping world. It turned out that the profit margins surged with the price, almost reaching 60% EBIT margins from below 10% pre-Covid.
EBIT_containers <- containers_EBIT %>%
#Some initial data wrangling of the date, to be able to plot it, now it's in quarter-year format but we need it as a date
mutate(year = substr(quarter, 4, length(quarter)),
quarter = substr(quarter, 1, 2),
month = ifelse(quarter == "Q1", 03, ifelse(quarter == "Q2", 06, ifelse(quarter == "Q3", 09, 12))),
year_month = as.Date(paste0(year, "-", month, "-", 30))) %>%
#Create a bar chart over the average profit margins
ggplot(aes(x = year_month, y = average_ebit_margin)) +
geom_col(fill = "#011F5F") +
#Remove background and borders
theme_minimal() +
#Add title, subtitle, axis labels and source
labs(title = "Significantly favouring the large container shipping companies",
subtitle = "Average EBIT margins for top container shipping companies, 2014-2023",
x = "Date",
y = "Average EBIT margin",
caption = "Source: Statista") +
#Set title, subtitle and caption size, font and colors
theme(plot.title = element_text(size = 52, family = "Roboto"),
plot.subtitle = element_text(size = 46, color = "grey50", family = "Roboto"),
plot.caption = element_text(size = 32, family = "Roboto")) +
#Left-align title
theme(plot.title.position = "plot") +
#Remove vertical gridlines and minor horizontal
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()) +
#Format the y-axis and manually set the limits and breaks
scale_y_continuous(labels = scales:: percent, limits = c(-0.1, 0.6), breaks = seq(-0.1, 0.6, by = 0.1) ) +
#Increase the size of the axis labels
theme(axis.text=element_text(size=40),
axis.title=element_text(size=40,face="bold")) +
#Expand the x axis slightly
scale_x_date(expand = c(0.1, 0.05))
#Display the plot
EBIT_containers